Clutter Removal in Ground Penetrating Radar by Learned RPCA

Samet Ozgul*, Isin Erer

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

Özet

Clutter suppression presents crucial importance for Ground Penetrating Radar (GPR) images since clutter decreases considerably target detection rates. Robust Principal Component Analysis (RPCA) is widely used to remove clutter. However, RPCA requires sequential singular value decomposition (SVD) operations in each iteration, and thus computational cost and run-time increase. Also, the hyperparameter should be set manually. In this paper we propose to use unfolding techniques by converting each iteration to a single layer of the network and train the resulting Convolutional Neural Network (CNN) structure to learn the separation of GPR images into clutter and target components. The proposed method is compared to SVD, traditional RPCA , SVD free RNMF and learning based RAE. The recently introduced public hybrid dataset is used for training. The visual and quantitative results validate a performance which approximates RPCA while outperforming RAE with running times less than in any of the existing methods.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2023 46th International Conference on Telecommunications and Signal Processing, TSP 2023
EditörlerNorbert Herencsar
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar183-186
Sayfa sayısı4
ISBN (Elektronik)9798350303964
DOI'lar
Yayın durumuYayınlandı - 2023
Etkinlik46th International Conference on Telecommunications and Signal Processing, TSP 2023 - Virtual, Online, Czech Republic
Süre: 12 Tem 202314 Tem 2023

Yayın serisi

Adı2023 46th International Conference on Telecommunications and Signal Processing, TSP 2023

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???event.eventtypes.event.conference???46th International Conference on Telecommunications and Signal Processing, TSP 2023
Ülke/BölgeCzech Republic
ŞehirVirtual, Online
Periyot12/07/2314/07/23

Bibliyografik not

Publisher Copyright:
© 2023 IEEE.

Finansman

V. ACKNOWLEDGMENT This work was funded by the Scientific and Technological Research Council of Turkey (TUBITAK) under Project No.120E234.

FinansörlerFinansör numarası
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu120E234

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